Overview

Dataset statistics

Number of variables14
Number of observations26064
Missing cells15989
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Nacelle ambient temperature (°C) is highly overall correlated with Metal particle count counterHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Metal particle count counter is highly overall correlated with Nacelle ambient temperature (°C)High correlation
blade_angle has 2243 (8.6%) missing valuesMissing
Rear bearing temperature (°C) has 2243 (8.6%) missing valuesMissing
Nacelle ambient temperature (°C) has 2243 (8.6%) missing valuesMissing
Front bearing temperature (°C) has 2243 (8.6%) missing valuesMissing
Tower Acceleration X (mm/ss) has 2243 (8.6%) missing valuesMissing
Tower Acceleration y (mm/ss) has 2243 (8.6%) missing valuesMissing
Metal particle count counter has 2243 (8.6%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 9429 (36.2%) zerosZeros
Rotor speed (RPM) has 1078 (4.1%) zerosZeros

Reproduction

Analysis started2023-07-08 12:00:48.210001
Analysis finished2023-07-08 12:01:04.554171
Duration16.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct26064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.8 KiB
Minimum2021-01-01 00:00:00
Maximum2021-06-30 23:50:00
2023-07-08T17:31:04.608075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:04.704287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct25862
Distinct (%)99.4%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean579.76497
Minimum-17.010895
Maximum2076.3541
Zeros1
Zeros (%)< 0.1%
Negative3883
Negative (%)14.9%
Memory size203.8 KiB
2023-07-08T17:31:04.811411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-17.010895
5-th percentile-1.6763904
Q169.819445
median297.87552
Q3893.48716
95-th percentile2014.4637
Maximum2076.3541
Range2093.365
Interquartile range (IQR)823.66772

Descriptive statistics

Standard deviation654.3814
Coefficient of variation (CV)1.1287012
Kurtosis-0.091099406
Mean579.76497
Median Absolute Deviation (MAD)291.90799
Skewness1.1168336
Sum15083165
Variance428215.01
MonotonicityNot monotonic
2023-07-08T17:31:05.013895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9499999881 9
 
< 0.1%
-0.9700000286 8
 
< 0.1%
-0.9800000191 7
 
< 0.1%
-0.9900000095 7
 
< 0.1%
-1.049999952 6
 
< 0.1%
-1.039999962 5
 
< 0.1%
-0.1700000018 4
 
< 0.1%
-0.2599999905 4
 
< 0.1%
-0.9399999976 4
 
< 0.1%
-0.6600000262 4
 
< 0.1%
Other values (25852) 25958
99.6%
(Missing) 48
 
0.2%
ValueCountFrequency (%)
-17.01089548 1
< 0.1%
-16.9036684 1
< 0.1%
-13.96000607 1
< 0.1%
-13.87834375 1
< 0.1%
-13.83835303 1
< 0.1%
-13.8109829 1
< 0.1%
-13.4926564 1
< 0.1%
-12.88230009 1
< 0.1%
-12.76811902 1
< 0.1%
-12.3682401 1
< 0.1%
ValueCountFrequency (%)
2076.354102 1
< 0.1%
2071.543732 1
< 0.1%
2071.330328 1
< 0.1%
2071.13454 1
< 0.1%
2070.993115 1
< 0.1%
2070.370483 1
< 0.1%
2070.156555 1
< 0.1%
2070.052509 1
< 0.1%
2069.932996 1
< 0.1%
2069.889276 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct25789
Distinct (%)99.1%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean178.96162
Minimum0.012630695
Maximum359.99014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:05.108320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.012630695
5-th percentile17.668149
Q173.980208
median199.63007
Q3262.71806
95-th percentile336.2515
Maximum359.99014
Range359.97751
Interquartile range (IQR)188.73785

Descriptive statistics

Standard deviation103.73401
Coefficient of variation (CV)0.57964387
Kurtosis-1.2742616
Mean178.96162
Median Absolute Deviation (MAD)88.912552
Skewness-0.15400588
Sum4655865.4
Variance10760.744
MonotonicityNot monotonic
2023-07-08T17:31:05.204067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.25999832 4
 
< 0.1%
5.880000114 4
 
< 0.1%
41.22000122 3
 
< 0.1%
40.31000137 3
 
< 0.1%
46.04000092 3
 
< 0.1%
10.56999969 3
 
< 0.1%
40.79999924 3
 
< 0.1%
39.50999832 3
 
< 0.1%
39.5 3
 
< 0.1%
40.81000137 3
 
< 0.1%
Other values (25779) 25984
99.7%
(Missing) 48
 
0.2%
ValueCountFrequency (%)
0.01263069545 1
< 0.1%
0.03999999911 1
< 0.1%
0.05713529601 1
< 0.1%
0.09630900663 1
< 0.1%
0.09671583136 1
< 0.1%
0.1230266028 1
< 0.1%
0.1353858932 1
< 0.1%
0.1434239476 1
< 0.1%
0.148892914 1
< 0.1%
0.1635386649 1
< 0.1%
ValueCountFrequency (%)
359.9901399 1
< 0.1%
359.9248281 1
< 0.1%
359.9229937 1
< 0.1%
359.9062241 1
< 0.1%
359.8595846 1
< 0.1%
359.8322633 1
< 0.1%
359.8245473 1
< 0.1%
359.8110958 1
< 0.1%
359.8079516 1
< 0.1%
359.7983034 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct6546
Distinct (%)25.2%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean180.39933
Minimum0.20379647
Maximum359.94527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:05.307230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20379647
5-th percentile15.93
Q174.097717
median201.41431
Q3263.97601
95-th percentile337.51181
Maximum359.94527
Range359.74147
Interquartile range (IQR)189.8783

Descriptive statistics

Standard deviation103.98162
Coefficient of variation (CV)0.57639693
Kurtosis-1.2610307
Mean180.39933
Median Absolute Deviation (MAD)90.170393
Skewness-0.16760558
Sum4693269
Variance10812.177
MonotonicityNot monotonic
2023-07-08T17:31:05.403429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
295.8048706 190
 
0.7%
43.36999893 157
 
0.6%
265.0725403 153
 
0.6%
48.85385132 140
 
0.5%
87.2689209 127
 
0.5%
298 120
 
0.5%
195.927002 110
 
0.4%
256.2920227 104
 
0.4%
212.3904724 100
 
0.4%
184.9513702 99
 
0.4%
Other values (6536) 24716
94.8%
ValueCountFrequency (%)
0.2037964742 1
 
< 0.1%
0.5606696606 4
< 0.1%
0.5611712933 2
 
< 0.1%
0.561186552 4
< 0.1%
0.5611877441 6
< 0.1%
0.5617071986 1
 
< 0.1%
0.733231233 1
 
< 0.1%
0.8993000368 1
 
< 0.1%
0.9098957091 1
 
< 0.1%
1.020607835 1
 
< 0.1%
ValueCountFrequency (%)
359.9452706 1
 
< 0.1%
359.9021821 1
 
< 0.1%
359.8206165 1
 
< 0.1%
359.769989 1
 
< 0.1%
359.7014348 1
 
< 0.1%
359.6232194 1
 
< 0.1%
359.5664227 1
 
< 0.1%
359.4641418 2
 
< 0.1%
359.463623 16
0.1%
359.4631042 17
0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9293
Distinct (%)39.0%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.8578206
Minimum-82.586662
Maximum92.703334
Zeros9429
Zeros (%)36.2%
Negative4
Negative (%)< 0.1%
Memory size203.8 KiB
2023-07-08T17:31:05.506850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-82.586662
5-th percentile0
Q10
median0.16383334
Q33.672
95-th percentile44.996667
Maximum92.703334
Range175.29
Interquartile range (IQR)3.672

Descriptive statistics

Standard deviation19.977396
Coefficient of variation (CV)2.2553398
Kurtosis6.6916766
Mean8.8578206
Median Absolute Deviation (MAD)0.16383334
Skewness2.6319741
Sum211002.14
Variance399.09633
MonotonicityNot monotonic
2023-07-08T17:31:05.602642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9429
36.2%
44.99333445 1567
 
6.0%
44.99666723 661
 
2.5%
89.99333191 489
 
1.9%
0.02466666698 256
 
1.0%
1.49333334 147
 
0.6%
92.67666626 106
 
0.4%
0.02483333349 105
 
0.4%
0.04933333397 104
 
0.4%
1.49666667 100
 
0.4%
Other values (9283) 10857
41.7%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
-82.58666229 2
 
< 0.1%
-45.71911942 1
 
< 0.1%
-12.53916346 1
 
< 0.1%
0 9429
36.2%
0.0001666666629 12
 
< 0.1%
0.0003333333259 10
 
< 0.1%
0.0003508771851 2
 
< 0.1%
0.0004999999888 7
 
< 0.1%
0.0004999999888 3
 
< 0.1%
0.0005263157777 1
 
< 0.1%
ValueCountFrequency (%)
92.70333354 1
 
< 0.1%
92.70333354 1
 
< 0.1%
92.67666626 106
0.4%
92.53000132 26
 
0.1%
92.49333191 8
 
< 0.1%
92.1453331 1
 
< 0.1%
92.12333425 15
 
0.1%
92.1166687 6
 
< 0.1%
92.11666616 4
 
< 0.1%
92.11666616 4
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19767
Distinct (%)83.0%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean61.247966
Minimum10.42
Maximum73.164999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:05.703905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.42
5-th percentile34.185
Q159.43158
median66.155
Q368.47
95-th percentile70.510001
Maximum73.164999
Range62.745
Interquartile range (IQR)9.0384201

Descriptive statistics

Standard deviation11.89917
Coefficient of variation (CV)0.19427861
Kurtosis3.8645679
Mean61.247966
Median Absolute Deviation (MAD)3.0274988
Skewness-2.0376635
Sum1458987.8
Variance141.59024
MonotonicityNot monotonic
2023-07-08T17:31:05.801694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.47000008 7
 
< 0.1%
69.27749977 7
 
< 0.1%
66.74500008 6
 
< 0.1%
66.44500008 6
 
< 0.1%
67.92250023 6
 
< 0.1%
69.53749962 6
 
< 0.1%
66.99000053 6
 
< 0.1%
67.05999985 6
 
< 0.1%
69.29499969 6
 
< 0.1%
70 6
 
< 0.1%
Other values (19757) 23759
91.2%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
10.41999969 1
< 0.1%
10.52000008 2
< 0.1%
10.61000032 1
< 0.1%
10.6500001 1
< 0.1%
10.69249978 1
< 0.1%
10.7775001 1
< 0.1%
10.79750018 1
< 0.1%
10.80000019 2
< 0.1%
10.82500019 1
< 0.1%
10.8900001 1
< 0.1%
ValueCountFrequency (%)
73.16499939 1
< 0.1%
73.02749977 1
< 0.1%
72.96999969 1
< 0.1%
72.91500015 1
< 0.1%
72.87749939 1
< 0.1%
72.86499901 1
< 0.1%
72.83250008 1
< 0.1%
72.77249985 1
< 0.1%
72.50249977 1
< 0.1%
72.49750023 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23490
Distinct (%)90.3%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean9.7273923
Minimum0
Maximum15.3023
Zeros1078
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:05.904028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.093285359
Q18.1497872
median9.8915732
Q313.663272
95-th percentile15.161681
Maximum15.3023
Range15.3023
Interquartile range (IQR)5.5134851

Descriptive statistics

Standard deviation4.5355851
Coefficient of variation (CV)0.46626937
Kurtosis-0.12499053
Mean9.7273923
Median Absolute Deviation (MAD)2.2982304
Skewness-0.79278988
Sum253067.84
Variance20.571532
MonotonicityNot monotonic
2023-07-08T17:31:05.999339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1078
 
4.1%
8.140000343 233
 
0.9%
8.149999619 55
 
0.2%
8.159999847 25
 
0.1%
8.18999958 14
 
0.1%
8.199999809 14
 
0.1%
8.170000076 14
 
0.1%
8.220000267 12
 
< 0.1%
8.180000305 11
 
< 0.1%
8.399999619 11
 
< 0.1%
Other values (23480) 24549
94.2%
(Missing) 48
 
0.2%
ValueCountFrequency (%)
0 1078
4.1%
0.0005170001095 1
 
< 0.1%
0.001459500374 1
 
< 0.1%
0.002650500275 1
 
< 0.1%
0.009412502171 1
 
< 0.1%
0.01050000242 6
 
< 0.1%
0.0110000018 8
 
< 0.1%
0.01150000188 9
 
< 0.1%
0.01200000197 3
 
< 0.1%
0.01250000205 3
 
< 0.1%
ValueCountFrequency (%)
15.30230024 1
< 0.1%
15.29810196 1
< 0.1%
15.28839661 1
< 0.1%
15.27473277 1
< 0.1%
15.27121671 1
< 0.1%
15.27075224 1
< 0.1%
15.26937363 1
< 0.1%
15.26921681 1
< 0.1%
15.26669665 1
< 0.1%
15.26536354 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct25790
Distinct (%)99.1%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1154.9135
Minimum0
Maximum1811.7513
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:06.100617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.25035
Q1969.52331
median1175.5352
Q31620.2867
95-th percentile1796.5019
Maximum1811.7513
Range1811.7513
Interquartile range (IQR)650.76338

Descriptive statistics

Standard deviation536.52992
Coefficient of variation (CV)0.46456284
Kurtosis-0.11638884
Mean1154.9135
Median Absolute Deviation (MAD)270.74982
Skewness-0.79900306
Sum30046231
Variance287864.35
MonotonicityNot monotonic
2023-07-08T17:31:06.194217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
< 0.1%
969.9299927 11
 
< 0.1%
969.9400024 9
 
< 0.1%
969.9500122 9
 
< 0.1%
969.9799805 8
 
< 0.1%
970.0100098 8
 
< 0.1%
970.1099854 8
 
< 0.1%
970.0999756 8
 
< 0.1%
969.9699707 8
 
< 0.1%
970.1500244 7
 
< 0.1%
Other values (25780) 25928
99.5%
(Missing) 48
 
0.2%
ValueCountFrequency (%)
0 12
< 0.1%
0.01999999955 1
 
< 0.1%
0.02999999933 1
 
< 0.1%
0.1299999952 1
 
< 0.1%
0.1700000018 1
 
< 0.1%
0.7400000095 1
 
< 0.1%
0.7461749045 1
 
< 0.1%
0.750583238 1
 
< 0.1%
0.7645977801 1
 
< 0.1%
0.7654750412 1
 
< 0.1%
ValueCountFrequency (%)
1811.751337 1
< 0.1%
1809.331312 1
< 0.1%
1809.262299 1
< 0.1%
1809.172036 1
< 0.1%
1808.941229 1
< 0.1%
1808.789667 1
< 0.1%
1808.772752 1
< 0.1%
1808.736631 1
< 0.1%
1808.638321 1
< 0.1%
1808.591143 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18556
Distinct (%)77.9%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.5688921
Minimum-3.3
Maximum27.52
Zeros0
Zeros (%)0.0%
Negative888
Negative (%)3.4%
Memory size203.8 KiB
2023-07-08T17:31:06.293732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.3
5-th percentile0.5025
Q14.6099999
median8.1225002
Q311.49
95-th percentile19.697369
Maximum27.52
Range30.82
Interquartile range (IQR)6.8800001

Descriptive statistics

Standard deviation5.555939
Coefficient of variation (CV)0.64838475
Kurtosis0.13292798
Mean8.5688921
Median Absolute Deviation (MAD)3.4275001
Skewness0.58211471
Sum204119.58
Variance30.868458
MonotonicityNot monotonic
2023-07-08T17:31:06.384177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 28
 
0.1%
4.5 27
 
0.1%
6.800000191 24
 
0.1%
9 23
 
0.1%
6.400000095 22
 
0.1%
2 22
 
0.1%
3.799999952 22
 
0.1%
7.400000095 21
 
0.1%
10.69999981 21
 
0.1%
4.599999905 21
 
0.1%
Other values (18546) 23590
90.5%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
-3.299999952 2
< 0.1%
-3.239999902 1
 
< 0.1%
-3.214999902 1
 
< 0.1%
-3.099999905 4
< 0.1%
-3.059999943 1
 
< 0.1%
-3.022499979 1
 
< 0.1%
-3.007499993 1
 
< 0.1%
-2.985000002 1
 
< 0.1%
-2.829999971 1
 
< 0.1%
-2.792499959 1
 
< 0.1%
ValueCountFrequency (%)
27.51999989 1
< 0.1%
27.48500004 1
< 0.1%
27.47500029 1
< 0.1%
27.36750031 1
< 0.1%
26.81842101 1
< 0.1%
26.79749956 1
< 0.1%
26.79499931 1
< 0.1%
26.76842117 1
< 0.1%
26.76000004 1
< 0.1%
26.75249977 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20095
Distinct (%)84.4%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean63.195857
Minimum10.6
Maximum77.245002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:06.484264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile34.429999
Q157.78
median69.3325
Q372.695
95-th percentile74.4625
Maximum77.245002
Range66.645002
Interquartile range (IQR)14.915

Descriptive statistics

Standard deviation13.456528
Coefficient of variation (CV)0.2129337
Kurtosis1.9326235
Mean63.195857
Median Absolute Deviation (MAD)4.4224991
Skewness-1.5628535
Sum1505388.5
Variance181.07814
MonotonicityNot monotonic
2023-07-08T17:31:06.581036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.39999962 10
 
< 0.1%
71.3625 7
 
< 0.1%
11.30000019 7
 
< 0.1%
73.66750031 7
 
< 0.1%
73.79249992 6
 
< 0.1%
71.60500031 6
 
< 0.1%
73.59999847 6
 
< 0.1%
73.35749969 5
 
< 0.1%
72.70249977 5
 
< 0.1%
72.91499977 5
 
< 0.1%
Other values (20085) 23757
91.1%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
10.60000038 2
 
< 0.1%
10.63250017 1
 
< 0.1%
10.69999981 1
 
< 0.1%
10.71499987 1
 
< 0.1%
10.80000019 2
 
< 0.1%
11 5
< 0.1%
11.00500002 1
 
< 0.1%
11.00750003 1
 
< 0.1%
11.0775003 1
 
< 0.1%
11.10000038 4
< 0.1%
ValueCountFrequency (%)
77.24500198 1
< 0.1%
77.24249954 1
< 0.1%
77.1200016 1
< 0.1%
77.09749985 1
< 0.1%
77.08999901 1
< 0.1%
77.07249947 1
< 0.1%
77.04750023 1
< 0.1%
77.04749985 1
< 0.1%
77.03750076 1
< 0.1%
76.98250084 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23821
Distinct (%)100.0%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean51.42616
Minimum3.2936446
Maximum237.30253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:06.683936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.2936446
5-th percentile4.8415677
Q129.787039
median49.965137
Q370.56068
95-th percentile104.94527
Maximum237.30253
Range234.00889
Interquartile range (IQR)40.773641

Descriptive statistics

Standard deviation30.751028
Coefficient of variation (CV)0.59796469
Kurtosis0.26166354
Mean51.42616
Median Absolute Deviation (MAD)20.380797
Skewness0.47533537
Sum1225022.6
Variance945.62572
MonotonicityNot monotonic
2023-07-08T17:31:06.897320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.275112414 1
 
< 0.1%
46.19599719 1
 
< 0.1%
42.17258472 1
 
< 0.1%
70.94716196 1
 
< 0.1%
60.86558514 1
 
< 0.1%
74.30173674 1
 
< 0.1%
38.33180223 1
 
< 0.1%
53.33648252 1
 
< 0.1%
40.22983909 1
 
< 0.1%
50.37175932 1
 
< 0.1%
Other values (23811) 23811
91.4%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
3.293644614 1
< 0.1%
3.405331819 1
< 0.1%
3.500910413 1
< 0.1%
3.510657091 1
< 0.1%
3.526414133 1
< 0.1%
3.612701637 1
< 0.1%
3.626959705 1
< 0.1%
3.633723903 1
< 0.1%
3.665607977 1
< 0.1%
3.666814965 1
< 0.1%
ValueCountFrequency (%)
237.3025331 1
< 0.1%
219.2286594 1
< 0.1%
211.4563595 1
< 0.1%
201.0873192 1
< 0.1%
199.5423821 1
< 0.1%
198.5468016 1
< 0.1%
198.110191 1
< 0.1%
197.6972841 1
< 0.1%
195.3128851 1
< 0.1%
194.3543957 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct24492
Distinct (%)94.1%
Missing48
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.750563
Minimum0.22989389
Maximum22.173118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:06.993085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.22989389
5-th percentile1.9114531
Q13.5943607
median5.3110058
Q37.4108484
95-th percentile11.289919
Maximum22.173118
Range21.943224
Interquartile range (IQR)3.8164878

Descriptive statistics

Standard deviation2.877248
Coefficient of variation (CV)0.50034196
Kurtosis0.37934798
Mean5.750563
Median Absolute Deviation (MAD)1.8696443
Skewness0.75282624
Sum149606.65
Variance8.2785558
MonotonicityNot monotonic
2023-07-08T17:31:07.087244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.769999981 11
 
< 0.1%
4.710000038 11
 
< 0.1%
4.519999981 10
 
< 0.1%
4.739999771 9
 
< 0.1%
3.589999914 9
 
< 0.1%
5.590000153 9
 
< 0.1%
4.550000191 8
 
< 0.1%
4.699999809 8
 
< 0.1%
5.519999981 8
 
< 0.1%
5.46999979 8
 
< 0.1%
Other values (24482) 25925
99.5%
(Missing) 48
 
0.2%
ValueCountFrequency (%)
0.2298938923 1
< 0.1%
0.2881313976 1
< 0.1%
0.3030564379 1
< 0.1%
0.3154877666 1
< 0.1%
0.3191063616 1
< 0.1%
0.3248439558 1
< 0.1%
0.3318188183 1
< 0.1%
0.3487688534 1
< 0.1%
0.3491065189 1
< 0.1%
0.3501376852 1
< 0.1%
ValueCountFrequency (%)
22.17311754 1
< 0.1%
20.40772481 1
< 0.1%
20.36893535 1
< 0.1%
20.05073695 1
< 0.1%
20.01316652 1
< 0.1%
19.68798828 1
< 0.1%
19.66393795 1
< 0.1%
19.35627661 1
< 0.1%
19.13497472 1
< 0.1%
18.97547688 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23821
Distinct (%)100.0%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean27.034713
Minimum3.4084855
Maximum167.22084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:07.182881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.4084855
5-th percentile4.7931461
Q115.479014
median23.49387
Q335.535509
95-th percentile60.533017
Maximum167.22084
Range163.81235
Interquartile range (IQR)20.056495

Descriptive statistics

Standard deviation17.209164
Coefficient of variation (CV)0.63655803
Kurtosis3.186967
Mean27.034713
Median Absolute Deviation (MAD)9.6489718
Skewness1.3232916
Sum643993.9
Variance296.15531
MonotonicityNot monotonic
2023-07-08T17:31:07.278456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.071377921 1
 
< 0.1%
21.16598797 1
 
< 0.1%
27.19546552 1
 
< 0.1%
26.21300657 1
 
< 0.1%
30.63056545 1
 
< 0.1%
25.88173113 1
 
< 0.1%
27.02796154 1
 
< 0.1%
22.45339723 1
 
< 0.1%
28.01536031 1
 
< 0.1%
24.91013227 1
 
< 0.1%
Other values (23811) 23811
91.4%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
3.408485544 1
< 0.1%
3.454908395 1
< 0.1%
3.482049316 1
< 0.1%
3.488504761 1
< 0.1%
3.499812649 1
< 0.1%
3.516291118 1
< 0.1%
3.52232554 1
< 0.1%
3.525328702 1
< 0.1%
3.58556065 1
< 0.1%
3.607324165 1
< 0.1%
ValueCountFrequency (%)
167.2208399 1
< 0.1%
165.9476103 1
< 0.1%
161.8787235 1
< 0.1%
161.8500399 1
< 0.1%
153.5184444 1
< 0.1%
148.812916 1
< 0.1%
143.6223341 1
< 0.1%
141.2701761 1
< 0.1%
139.2143726 1
< 0.1%
138.5975738 1
< 0.1%

Metal particle count counter
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing2243
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean712.65337
Minimum708
Maximum715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:31:07.363164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum708
5-th percentile708
Q1710
median714
Q3715
95-th percentile715
Maximum715
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3664754
Coefficient of variation (CV)0.0033206542
Kurtosis-1.2303361
Mean712.65337
Median Absolute Deviation (MAD)1
Skewness-0.51603989
Sum16976116
Variance5.6002058
MonotonicityIncreasing
2023-07-08T17:31:07.430386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
715 8100
31.1%
714 4681
18.0%
710 3883
14.9%
711 3005
 
11.5%
713 1453
 
5.6%
708 1403
 
5.4%
709 1056
 
4.1%
712 240
 
0.9%
(Missing) 2243
 
8.6%
ValueCountFrequency (%)
708 1403
 
5.4%
709 1056
 
4.1%
710 3883
14.9%
711 3005
 
11.5%
712 240
 
0.9%
713 1453
 
5.6%
714 4681
18.0%
715 8100
31.1%
ValueCountFrequency (%)
715 8100
31.1%
714 4681
18.0%
713 1453
 
5.6%
712 240
 
0.9%
711 3005
 
11.5%
710 3883
14.9%
709 1056
 
4.1%
708 1403
 
5.4%

Interactions

2023-07-08T17:31:02.829876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:48.928020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.002795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.223162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.365759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.519454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.790348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.956162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.097173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.298683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.430078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.502885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.576428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.911384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.005843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.084374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.303677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.448902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.599251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.869166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.036580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.172863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.377841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.505080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.578691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.656968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.001122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.090578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.171707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.394455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.537768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.687993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.961466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.128265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.260323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.467860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.588022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.661820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.747247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.092722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.175302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.261303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.482953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.628309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.779304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.051922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.220731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.345409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.557079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.673333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.747280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.836355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.181851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.258572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.349382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.573274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.718525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.868441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.143530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.312555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.432035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.645374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.756813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.831366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.926025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.271138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.342041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.437686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.661260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.814264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.955999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.234107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.404734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.515856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.732377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.840631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.913618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.014651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.363062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.430773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.529377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.754118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.906856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.050745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.328893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.497023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.605451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.823244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.927854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.001923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.222034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.453686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.514625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.621459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.843882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.997823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.143754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.420447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.582949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.690158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.913524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.013321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.085357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.309362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.539101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.594577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.706839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.929681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.082598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.231313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.507203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.667017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.770781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.997325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.092923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.166211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.395676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.631124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.679335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.802055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.019773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.174437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.324589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.600898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.755908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.857764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.086893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.179721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.252192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.486315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.712749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.756025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.883358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.102007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.255863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.410240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.683614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.837689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.936620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.167612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.255067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.328933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.568336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.794785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.830709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:50.963840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.183115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.337775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.497800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.769566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:56.917650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.013685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.249503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.330357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.404710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.648933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:03.886720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:49.916116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:51.131406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:52.274333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:53.426937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:54.590493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:55.861896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:57.005962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:58.099067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:30:59.338419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:00.416127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:01.489265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:02.738228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:31:07.509282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0440.032-0.3370.7570.9890.989-0.0710.9290.6390.9480.822-0.144
Wind direction (°)0.0441.0000.881-0.0210.0840.0440.0430.1140.0840.0020.0770.0500.054
Nacelle position (°)0.0320.8811.000-0.0070.0660.0300.0280.1050.065-0.0250.0660.0230.038
blade_angle-0.337-0.021-0.0071.000-0.550-0.346-0.3460.092-0.407-0.153-0.262-0.158-0.002
Rear bearing temperature (°C)0.7570.0840.066-0.5501.0000.7570.7540.0890.8490.4690.6960.5610.029
Rotor speed (RPM)0.9890.0440.030-0.3460.7571.0000.999-0.0670.9310.6450.9400.824-0.141
Generator RPM (RPM)0.9890.0430.028-0.3460.7540.9991.000-0.0800.9290.6460.9400.825-0.148
Nacelle ambient temperature (°C)-0.0710.1140.1050.0920.089-0.067-0.0801.000-0.041-0.054-0.124-0.0790.672
Front bearing temperature (°C)0.9290.0840.065-0.4070.8490.9310.929-0.0411.0000.5840.8730.750-0.099
Tower Acceleration X (mm/ss)0.6390.002-0.025-0.1530.4690.6450.646-0.0540.5841.0000.5660.867-0.087
Wind speed (m/s)0.9480.0770.066-0.2620.6960.9400.940-0.1240.8730.5661.0000.784-0.211
Tower Acceleration y (mm/ss)0.8220.0500.023-0.1580.5610.8240.825-0.0790.7500.8670.7841.000-0.141
Metal particle count counter-0.1440.0540.038-0.0020.029-0.141-0.1480.672-0.099-0.087-0.211-0.1411.000

Missing values

2023-07-08T17:31:04.015621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:31:04.202127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:31:04.405794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02021-01-01 00:00:00-1.338018304.833128313.36538789.99333215.6150000.01.4636801.18000017.3000004.2751126.2046567.071378708.0
12021-01-01 00:10:00-2.593343299.338570313.36538789.99333215.4450000.01.4083020.76500017.1725015.5350715.85888810.096306708.0
22021-01-01 00:20:00-1.319346299.172669313.36538789.99333215.4900000.03.1581010.05500017.1025006.5016736.06903812.734161708.0
32021-01-01 00:30:00-1.118750296.647785313.36538789.99333215.3400000.01.869531-0.30750017.0350005.5765825.91886911.858300708.0
42021-01-01 00:40:00-2.800342291.827335313.36538789.99333215.2200000.01.857184-0.24000016.88500012.0473605.40830612.953673708.0
52021-01-01 00:50:00-1.663453287.716855313.36538789.99333215.0550000.01.420518-0.11500016.71250010.2612005.5861699.655035708.0
62021-01-01 01:00:00-2.163046289.537212313.36538789.99333214.9555550.01.9298930.05277816.47777811.5946975.6289589.484116708.0
72021-01-01 01:10:00-2.196420294.658396313.36538789.99333214.7450000.01.7377640.10750016.3350005.9380585.7220879.621411708.0
82021-01-01 01:20:00-1.368961289.963821313.36538789.99333214.6575000.01.908790-0.05500016.1450017.1673395.30205012.092407708.0
92021-01-01 01:30:00-1.245723295.076614313.36538789.99333214.4900000.01.6797760.13000016.0350006.5707745.43440613.863467708.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
260542021-06-30 22:20:00-1.04000027.07000034.59NaNNaN0.6779.190002NaNNaNNaN2.07NaNNaN
260552021-06-30 22:30:00-0.96000025.37999934.59NaNNaN0.6172.760002NaNNaNNaN1.82NaNNaN
260562021-06-30 22:40:00-0.94000027.77000034.59NaNNaN0.6375.500000NaNNaNNaN1.76NaNNaN
260572021-06-30 22:50:00-1.27000034.11999934.59NaNNaN0.6679.339996NaNNaNNaN1.88NaNNaN
260582021-06-30 23:00:00-1.16000034.48000034.59NaNNaN0.89106.750000NaNNaNNaN2.51NaNNaN
260592021-06-30 23:10:0050.63999939.95000134.59NaNNaN6.94827.309998NaNNaNNaN3.84NaNNaN
260602021-06-30 23:20:0097.33000239.70999934.59NaNNaN8.14970.020020NaNNaNNaN4.13NaNNaN
260612021-06-30 23:30:0073.73000342.22000134.59NaNNaN8.14969.750000NaNNaNNaN3.78NaNNaN
260622021-06-30 23:40:00-9.37000041.73000034.59NaNNaN8.13968.440002NaNNaNNaN1.92NaNNaN
260632021-06-30 23:50:00-1.09000033.93000034.59NaNNaN0.90106.279999NaNNaNNaN2.02NaNNaN